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In [1]: import torch from transformers import AutoModel,AutoTokenizer, BertTokenizer print(torch.__version__) torch.set_grad_enabled(False) 1.6.0 Out[1]: In [2]: # 모델 저장하기 , Store the model we want to use MODEL_NAME = "bert-base-cased" # We need to create the model and tokenizer model = AutoModel.from_pretrained(MODEL_NAME) tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) print('model : ',m..

import json import numpy as np import pandas as pd from random import choice from keras_bert import load_trained_model_from_checkpoint, Tokenizer import re, os import codecs from Cleaning_Text import Cleaning_Text # ------------------------------------------------------- import codecs, gc from sklearn.model_selection import KFold from keras.metrics import top_k_categorical_accuracy from keras.la..

https://blog.csdn.net/asialee_bird/article/details/102747435 https://github.com/bojone/bert_in_keras/blob/master/sentiment.py https://search.gitee.com/?q=keras%20bert&skin=rec&type=repository https://www.kesci.com/home/project/5e78a11198d4a8002d2c52bc keras bert - Gitee 语言： 全部 排序： 最佳匹配 search.gitee.com bojone/bert_in_keras 在Keras下微调Bert的一些例子；some examples of bert in keras. Contribute to bojone/b..

""" 使用 PyTorch-Transformers 模型库，先设置好准备输入模型的例子， 使用 BertTokenizer() 建立分词器对象对原句进行分词，然后对照词表将词转换成序号。 """ # from nltk import sent_tokenize, word_tokenize from Cleaning_Text import Cleaning_Text import numpy as np import os, sys import pandas as pd print('success') ## 데이타 읽어 드리고 , 변환 하기 # df = pd.read_excel('./test_data_pytorch/test_allData_5000-2.xlsx') read_path='./../../04-srcTest/test_data/test_all..

from test import test_ import os, sys import pandas as pd # 참고하는 경로 조회 find_path = sys.path [print(x) for x in find_path] # import 된 모듈에 대한 경로를 확인하기 print(BertTokenizer) # import 된 모듈의 경로를 지정하여 읽어 드리기 df = pd.read_table('C:/app/Anaconda/anacondas/test_.py') # import 된 모듈의 지정된 경로 아래 파일을 오픈하기 os.startfile('C:/app/Anaconda/test_.py')